Based on our Unity implementation on top of Drone Flight, we conduct DRL-based agent trajectory training and performance evaluation. In addition, the results are visualized.
For the DRL training of the agent, proximal policy optimization (PPO) is used [1]}. The policy of agent is trained by the deep neural networks with 2 dense layers where each layer is with 128 units. In addition, \(\epsilon \) -greedy is used for DRL training exploration where \(\epsilon =0.2\) .
Furthermore, multi-agent parallel processing is utilized that is supported by mlagents, thus parallel accelerated training computation is realized. For the DRL training, learning iteration is set to \(3,000\)  K and detailed hardware/software specification is summarized in Table REF .
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